It was empirically shown in [1]} that interpolating kernel machines \(f_{\rm ker,q}\)  (see Eq. REF ) with common Laplace and Gaussian kernels, trained to interpolate \(q\) -corrupted data, generalizes nearly optimally (approaches the Bayes risk) to the similarly corrupted test data. An example of that is shown inFor a ten-class problem in panel (b), which makes the point even stronger. For simplicity, we only discuss a two-class analysis here. Fig. REF . In particular, we see that the Laplace kernel tracks the optimal Bayes error very closely, even when as much as \(80\%\)  of the data are corrupted (i.e., \(q=0.8\) ).
